Several techniques have been proposed to reduce the effects of domain shift on a model [1]}, [2]}, which can be divided into either domain adaptation methods (where we have a set of target domains with different data distribution) or domain generalisation methods (where we wish to make our model better with any unseen data). There are many possible directions that one could follow to apply these methods in deep learning, from using conventional algorithms and regularization during training, to modifying the model being used to learn more relevant features or creating additional methods to learn how to model and lessen the impact of domain shift [3]}.
